Neighborhood Fast-Food Environments and Hypertension in Canadian Adults
Bibliographic record
Abstract
INTRODUCTION: Hypertension is a leading cause of cardiovascular disease and premature death worldwide. Neighborhoods characterized by a high proportion of fast-food outlets may also contribute to hypertension in residents; however, limited research has explored these associations. This cross-sectional study assessed the associations between neighborhood fast-food environments, measured hypertension, and self-reported hypertension. METHODS: Data from 10,700 adults living in urban areas were obtained from six Canadian Health Measures Survey cycles (2007-2019). Each participant's blood pressure was measured at a mobile clinic six times. Measured hypertension was defined as having an average systolic blood pressure ≥140 or a diastolic blood pressure ≥90 mm Hg or being on blood pressure-lowering medication. Participants were also asked whether they had been diagnosed with high blood pressure or whether they take blood pressure-lowering medication (i.e., self-reported hypertension). The proportion of fast-food outlets relative to the sum of fast-food outlets and full-service restaurants in each participant's neighborhood was obtained from the Canadian Food Environment Dataset, and analyses were conducted in 2022. RESULTS: The mean proportion of fast-food outlets was 23.3% (SD=26.8%). A one SD increase in the proportion of fast-food outlets was associated with higher odds of measured hypertension in the full sample (OR=1.17, 95% CI=1.05, 1.31) and in sex-specific models (women: OR=1.14, 95% CI=1.01, 1.29; men: OR=1.21, 95% CI=1.03, 1.43). Associations between the proportion of fast-food outlets and self-reported hypertension were inconclusive. CONCLUSIONS: Findings suggest that reducing the proportion of fast-food restaurants in neighborhoods may be a factor that could help reduce hypertension rates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".